Zero of fourteen models named Decentriq first on the direct prompt; one named Databricks Clean Rooms. Decentriq was named by twelve of the fourteen models and Databricks Clean Rooms by twelve and Decentriq carries 28 labels and Databricks Clean Rooms 27, so the shares are not directly comparable.
Named in one category this edition.
By Databricks, San Francisco, United States, founded 2013. Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the data clean rooms page.
Across every category in the October 2026 Edition, Decentriq and Databricks Clean Rooms were named in the same answer thirty-six times, of the 87 answers naming Decentriq and the 73 naming Databricks Clean Rooms. In those answers Databricks Clean Rooms took the first choice one time and Decentriq two.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“positioned more for regulated or security-heavy use cases, which usually makes it less budget-oriented” Perplexity Sonar · budget prompt · soft negative
“these enterprise-grade platforms are almost always a classic case of over-engineering” Gemini 3.5 Flash · direct prompt · soft negative
“like LiveRamp (Habu), InfoSum, or Decentriq is rarely feasible” Gemini 3.5 Flash · budget prompt · soft negative
“neutral, pure-play DCR vendors with hardware-backed privacy ... These include: Decentriq (confidential computing/hardware-backed)” DeepSeek V4 Flash · negative prompt · first choice
“Optable or Decentriq are commonly cited data clean room options” Muse Glimmer 30B · paraphrase prompt · first choice
“I'd suggest starting with either Decentriq or InfoSum” MiniMax M2.5 · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Cloud-based (e.g., AWS Clean Rooms, Snowflake, Databricks) | Strong infrastructure, but integration complexity and variable TCO” Grok 4.1 Fast · negative prompt · soft negative
“Some concerns about identity resolution rules not being well-documented” Kimi K2 · negative prompt · soft negative
“user reviews of Databricks Clean Rooms have scored poorly here” DeepSeek V4 Flash · negative prompt · soft negative
“Snowflake, Databricks, and LiveRamp Safe Haven provide balanced scalability and governance support.” Claude Haiku 4.5 · direct prompt · first choice
“Choose Snowflake, Databricks, or AWS if you already have a mature data team” Gemini 3.5 Flash · comparative prompt · first choice
“If your data ecosystem is built on Databricks, their Delta Sharing and Unity Catalog offer a similar, highly secure distributed data clean room experience.” Gemini 3.5 Flash · paraphrase prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.